莫里哀:自动生物医学假设生成系统MOLIERE: Automatic Biomedical Hypothesis Generation System |
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课程网址: | http://videolectures.net/kdd2017_sybrandt_generation_system/ |
主讲教师: | Justin Sybrandt |
开课单位: | 克莱姆森大学 |
开课时间: | 2017-10-09 |
课程语种: | 英语 |
中文简介: | 假设生成正在成为一种重要的节省时间的技术,它使生物医学研究人员能够快速发现重要概念之间的隐含联系。通常,这些系统对公共医疗数据的特定领域部分进行操作。相比之下,莫里哀利用了超过 2450 万份文档中的信息。我们方法的核心是从国家生物技术信息中心(NCBI)的多个异构数据集中提取的生物医学对象的多模式和多关系网络。这些对象包括但不限于科学论文、关键词、基因、蛋白质、疾病和诊断。我们使用潜在狄利克雷分配对在该网络中发现的最短路径附近发现的摘要进行假设建模,并通过对历史数据进行假设生成来证明莫里哀的有效性。我们的网络、实施和结果数据均可供广大科学界公开使用。 |
课程简介: | Hypothesis generation is becoming a crucial time-saving technique which allows biomedical researchers to quickly discover implicit connections between important concepts. Typically, these systems operate on domain-specific fractions of public medical data. MOLIERE, in contrast, utilizes information from over 24.5 million documents. At the heart of our approach lies a multi-modal and multi-relational network of biomedical objects extracted from several heterogeneous datasets from the National Center for Biotechnology Information (NCBI). These objects include but are not limited to scientific papers, keywords, genes, proteins, diseases, and diagnoses. We model hypotheses using Latent Dirichlet Allocation applied on abstracts found near shortest paths discovered within this network, and demonstrate the effectiveness of MOLIERE by performing hypothesis generation on historical data. Our network, implementation, and resulting data are all publicly available for the broad scientific community. |
关 键 词: | 假设生成; 假设建模; 数据科学 |
课程来源: | 视频讲座网 |
数据采集: | 2023-12-25:wujk |
最后编审: | 2023-12-25:wujk |
阅读次数: | 16 |